The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches. In terms of context-aware session modeling, however, the existing RNN-based models are limited in that they are not designed to explicitly model rich static user-side contexts (e.g., age, gender, location). Therefore, in this paper, we explore the utility of explicit user-side context modeling for RNN session models. Specifically, we propose an augmented RNN (ARNN) model that extracts high-order user-contextual preference using the product-based neural network (PNN) in order to augment any existing RNN session model. Evaluation results show that our proposed model outperforms the baseline RNN session model by a large margin when rich user-side contexts are available.
@article{arxiv.1805.02983,
title = {Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation},
author = {Younghun Song and Jae-Gil Lee},
journal= {arXiv preprint arXiv:1805.02983},
year = {2018}
}